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Questions tagged [multilabel-classification]

Multilabel classification assigns to each sample a set of target labels. This can be thought as predicting properties of a data-point that are not mutually exclusive, such as topics that are relevant for a document. A text might be about any of religion, politics, finance or education at the same time or none of these.

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144 views

How to train on extended data set correctly

I have trained my classifier on pictures with a mixture of several classes on each picture, e.g. A-F. The classifier is able to (nearly) correctly segment those classes on the images. Now I got more ...
0 votes
1 answer
536 views

calculate sklearn metrics from 2d array

I have the following frame of actual value, ...
0 votes
1 answer
178 views

Binary classificaiton for weather data if its class 1 or class 0 alert

I am working on weather data and it has few features that are independent variables such as severity, severity_id, ...
2 votes
1 answer
38 views

Noob question - which NLP/deep learning technique shoud I use

Let's say I have dataset with inputs and expected outputs like this: ...
2 votes
1 answer
57 views

Best Way to tackle to time series classification problem?

I have a dataset where the input is a dataset for ICU patients where each ICU stay has 40 features (20 vitals, 20 lab values) and multiple time steps (the stays' length is between 6 and 19-time steps)....
1 vote
1 answer
196 views

Trained CNN individually on multiple images to classify them, how can I now classify a related "set" of these images that correspond to one object?

I have a N object classification examples, each example consisting of a set M individual images of the object at different angles. I've trained M CNNs with the dataset of one particular image angle ...
0 votes
1 answer
191 views

We pose recommendation as extreme multiclass classification problem, what is a class here? is it video category? or the video itself?

In the Youtube video recommendation paper, the author talks about candidate generation is a multi class classification problem, I am trying to understand what the classes here, a video category or the ...
2 votes
1 answer
2k views

Using LSTM for multi label classification

I am trying to use LSTMs to train and predict authors using reviews data and metadata ...
4 votes
1 answer
2k views

Why does classifier chain ask for at least 2 classes, when I have it

I'm using Classifier Chain with logistic regression and when i try to use fit, i get This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 but I'm ...
1 vote
1 answer
308 views

How to explain a stable NDCG@K in extreme multilabel recommender model

I am working in a multilabel recommender project and I try to evaluate it as a ranking problem. I calculate recall@k and precision@k which both looks quite well. Recall increases and Precision ...
0 votes
1 answer
310 views

Transform multi-class problem to multi-label problem

I found this question but I need an answer to the other direction. Example: Let's say we want to predict if a person with a certain profile wants to buy product A and/or B. So we have 2 binary classes ...
0 votes
2 answers
2k views

How to perform Multi-Label Image Classification with EfficientNet

Problem My goal is to perform multi-label image classification with EfficientNet. It should take a picture as input and e.g. tell the user that it sees a person AND a dog on the picture, meaning the ...
2 votes
1 answer
348 views

Multilabel Classification - Overfitting?

My task is the following: To input drug combinations and output renal failure-related symptoms from the drug combinations. Both the drug combinations and renal-failure related symptoms are represented ...
2 votes
1 answer
88 views

How to do a Multilabel classification where the label order is important?

I am doing carbon composite modelling for my college project. Each composite sample is created by stacking carbon fiber of different angles (0, 45, 90,-45). A sample can contain 8, 12 or 16 of such ...
2 votes
1 answer
66 views

How does dependency information impact binary classification in multi-label prediction models?

TL;DR: I don't understand the dependency issue with binary classification (binary relevance) compared to multi-label prediction models. I often read in papers that some kind of "dependency ...
0 votes
1 answer
162 views

Evaluating model with categorical target variables

I converted all the numeric target variables of MNIST dataset into categorical variables. So, 0 became zero, and so on. Next, I ...
0 votes
1 answer
70 views

Fine-tune zero-shot classification model multi-label

I started a small project where I am trying to fine-tune a zero-shot classification model on a proprietary dataset. I was thinking to use the NLI approach, building contradiction and entailment ...
0 votes
0 answers
13 views

How to organize multi-layer data in Orange Data Mining?

I have data in the form of a MATLAB cell array in which: Rows are individual ROIs columns are image channels But each element of each column stores not only the mean intensity value of the ROI, but ...
0 votes
0 answers
30 views

Multilabel Classification - Flat Binary Classifiers vs Hierarchical Binary Classifiers

Was researching on multi label classification to solve the problem of tagging news articles with topics and countries, where tags follow the syntax <topic>-<country>, and would like to ...
0 votes
0 answers
7 views

What are approaches to identify the meaning of columns in a dataframe based on similarity to known column instances

In my domain we can perform upon to 12 tests on a substance, and record results for each of the tests at different pressures e.g. between 10 and 20 steps between 0 and 6000 psi. for each substance ...
0 votes
0 answers
18 views

Loss increase while accuracy also increase [duplicate]

I'm training a fairly large classification model,and I'm having the below results. ...
1 vote
1 answer
130 views

Data quality improvement as a part of preprocessing: Imputation

I have a python pandas dataframe representing a superset. The data contains a lot of nulls which I want to overwrite with real values. the superset has: both numerical and categorical data some ...
0 votes
1 answer
282 views

TensorFlow Time Series Tutorial Enhancement Gone Wrong

I’ve been following this time series tutorial for Tensorflow… https://www.tensorflow.org/tutorials/structured_data/time_series And it was going well and seemed to work ok. I substituted with my own ...
0 votes
1 answer
347 views

Two-level (large category and small category) label classification problem

At present, there is an app classification task, the input is the function description of the app, and the two labels are the major category to which the app belongs and the small categories under the ...
1 vote
1 answer
384 views

Multilabel classification: Choosing threshold

I'm creating a multilabel classification approach based on sentence embeddings applied to text taken from a chatbot. We have the following: a training dataset of 2,500 lines, where each line is a ...
1 vote
1 answer
141 views

Which is better: multi-output model or separate models for similar tasks?

I am working on two problems: classification of images into high-level classes (e.g. shoe, dress, jacket etc.) classification of the attributes of the same images on a lower level (e.g. shoe style, ...
3 votes
2 answers
1k views

What is the better way to predict classes for the models developed using the functional API in Keras

We can predict the class for new data instances using the Sequential classification model in Keras using the predict_classes() function. What is the way to predict the class for models that developed ...
1 vote
1 answer
205 views

How to cluster label (in a multilabel classification problem) which mostly appear together in a class

To cluster label (in a multilabel classification problem) which mostly appear together in a dataframe? For example, I have this dataframe: ...
0 votes
0 answers
41 views

Is this classifier better than a random guess?

I'm working with the SAMHSA Mental Health Client-Level Dataset. I'm trying to train classifiers to predict the disorder given the rest of the columns. There are 14 binary disorder columns (bipolar, ...
0 votes
1 answer
496 views

What are the options/best practices for encoding categorical features for multilabel classification?

I am working on a multilabel classification problem with both continuous and categorical features. For a single label problem, I might make use of a supervised encoder for my categorical features such ...
1 vote
1 answer
64 views

Merge one label with one information for classification problem or multi-label classification

I want to build a model to support decision making in order to propose or not loan insurance to clients. Because sometimes clients asking loan and loan insurance have less chance to have their loan ...
13 votes
6 answers
35k views

How to use sklearn train_test_split to stratify data for multi-label classification?

I am attempting to mirror a machine learning program by Ahmed Besbes, but scaled up for multi-label classification. It seems that any attempt to stratify the data returns the following error: ...
2 votes
1 answer
64 views

Predicting if a search keyword is lower volume based only on high volume keywords

My friend was asked this question in an interview for analytics and I cannot figure out the answer so I would like to see how could this data science problem be solved. Here's the problem: Let's say ...
0 votes
1 answer
366 views

Mulitlabel stratified k-fold splitting with non-overlapping groups

For multilabel stratification, we have a good solution implemented by scikit-multilearn which I believe is based on the algorithm presented in "On the Stratification of Multi-label Data". ...
0 votes
1 answer
37 views

How to create a multi label text classification model for small dataset in production [closed]

I have a multi-label text classification dataset which is very small around 80Kb, I am only going to receive a small amount of data for training from my client. But it is expected to build a high ...
0 votes
2 answers
276 views

Multi-label classification with nested features

I need to perform a multi-label classification. I have three features and they are nested. I am unsure how to combine this or what kind of classification algorithm would be best. Some multi level ...
0 votes
0 answers
29 views

Variable length multi class multi label problem

I have to create a model that will output a variable number of tuples of size 3 as output. The tuples have to contain some category, not a float. I've never encountered a problem as this one so I'm ...
1 vote
1 answer
32 views

Multinomial Logistic Regression sensitive to choice of Encoding

I am using the following LogisticRegression model using sklearn. The task requires to select one label from multi-labels, so if I provide a, b the output could be <...
1 vote
2 answers
203 views

Positive/negative training sample imbalance in multi-label image classifiers

I'm trying to train VGG-16 on the Pascal VOC 2012 dataset, which has images with 20 labels (and a given image can have multiple classes present). The examples are highly imbalanced, so I've "...
0 votes
1 answer
41 views

Object localization and text extraction using VGG

I'm new to Computer Vision and training a TensorFlow neural network using VGG16. The problem is quite simple: I'm training in a custom dataset to detect and localize numbers in a 100x100 image. The ...
1 vote
1 answer
3k views

Clustering of multi-label data

The dataset consists of 1) a set of objects and 2) a set of labels, which are used to describe the objects. For the moment, for simplicity sake, each label can be marked as either true or false (...
0 votes
1 answer
27 views

Academic name of dataset preparation method with hierarchical-learned labels? - E.g., cold→half-cooked→cooked

What's the name of the dataset preparation method indicating hierarchical ontologies? Assume photos of cold, half-cooked, and fully-cooked chickens. Annotate with temperature data. E.g., at current ...
0 votes
0 answers
61 views

LSTM Layer producing same outputs for different sequences

Currently I try to train on a multi-label language task with imbalanced class distribution. I have the following model, where I removed some of the feed forward layers to decrease factors in the chain ...
0 votes
0 answers
82 views

Custom loss function for multi label classification in catboost?

I have a data frame which I want to use for multi class classification problem. There are total 6 classes (say a, b, c, d, e, f). I want to improve the precision for three classes (say a, b, c) i.e. ...
1 vote
0 answers
37 views

Is it possible to determine the probability of each time sample to belong a certain class using gaussian distribution with Recurrent Neural Networks?

I'm trying to train a deep learning model that predicts the probability of each time sample in a two-component time series . In this case, I want the target tensor (Y) to be a probability value for ...
0 votes
1 answer
154 views

How can I labelling a sequence of network traffic to one single classification?

I want to labelling network traffic (several .pcap-files) to different classifications. But this network traffic are not just one entry, there are sequence of entries (~50). So how is it possible, to ...
0 votes
0 answers
23 views

How to Fix Dimension Issues of features and classes from a Multilabel Classification dataset in getting the Out-of-Bag Error of a Random Forest?

I have created a multilabel classification dataset using make_multilabel_classification from scikit learn: ...
0 votes
0 answers
149 views

How to deal with "Could not broadcast input array from shape (1141,2) into shape (1141,)" to get Out-of-Bag error while using Random Forest

I have a dataset that consists of 171 features and 39 labels. I captured both features and labels of the dataset through slicing: ...
0 votes
0 answers
15 views

Modeling Individual Device Demand in a Domestic Electrical Installation using Machine Learning

I'm working on a machine learning project aimed at classifying electrical loads detected in a domestic electrical installation by a current transformer (CT) during daily activities. The challenge lies ...
0 votes
0 answers
27 views

Training a two-layer neural network for multi-label data (binary bit array of dim 50)

This is my problem setup. Train Input size (6300x300) These are standard BERT embeddings, so floating point numbers, mostly negatives. Train Output size (6300x50) These are binary bit arrays like [0, ...

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